The clinical significance of silent mutations with respect to ciprofloxacin resistance in MRSA

Analysis of CDR1 and MDR1 Gene Expression and ERG11 Substitutions in Clinical Candida tropicalis Isolates from Alexandria, Egypt

INTRODUCTION

Candida tropicalis is a common non-albicans Candida (NAC) species that causes numerous fungal infections. Increasing antifungal resistance to azoles in NAC is becoming a major health problem worldwide; however, in Egypt, almost no data is available regarding fluconazole resistance mechanisms in C. tropicalis. The current study aims to investigate two possible important molecular mechanisms involved in fluconazole resistance in C. tropicalis isolates.

MATERIALS

Fifty-four clinical C. tropicalis isolates were included. Identification and antifungal susceptibility profiles of the isolates were carried out using the VITEK 2 compact system. The molecular investigation of fluconazole resistance included the expression of the CDR1 and MDR1 genes by quantitative real-time RT-PCR as well as the sequence analysis of the ERG11 gene.

RESULTS

Antifungal susceptibility testing identified 30 fluconazole-non-susceptible isolates. Statistically, CDR1 gene expression in fluconazole-non-susceptible isolates was significantly higher than that in fluconazole-susceptible isolates, with MDR1 gene expression levels that were similar in both non-susceptible and susceptible isolates. Sequence analysis of the ERG11 gene of 26 fluconazole-resistant isolates identified two missense mutations: A395T (Y132F) and G1390A (G464S).

CONCLUSIONS

This study has highlighted the role of overexpression of the CDR1 gene and ERG11 gene mutations in fluconazole non-susceptibility. Further studies in Egypt are required to investigate other possible molecular mechanisms involved in azole resistance.

Unraveling the mechanisms of Cefoxitin resistance in methicillin-resistant Staphylococcus aureus (MRSA): structural and molecular simulation-based insights

Methicillin-resistant Staphylococcus aureus (MRSA) severely affects human health, including the skin glands, nasal cavity, wound infections, bone infections, and pneumonia. Among the most effective MRSA drugs, Cefoxitin also develops resistance due to mutations in the mecA gene. Four mutations at positions E229K, E239R, G246K, and E447K are classified as high-level resistance mutations. However, the resistance mechanism of MRSA towards Cefoxitin caused by these mutations is still unclear, as there is less information available regarding the structural and functional effects of the mutations against Cefoxitin. Therefore, our present study was designed to explore the mechanisms of binding interactions between wild-type and mutated PBP2a against Cefoxitin using molecular docking and MD simulations. Subsequently, we identified that the mutant form of PBP2a affects the activity of Cefoxitin. Interestingly, the binding of Cefoxitin with G246K and E239R mutants demonstrates unstable behavior compared to E447K-Cefoxitin and E229K-Cefoxitin. In this study, we propose the resistance mechanism of Cefoxitin at the atomic level. The proposed drug-resistance mechanism will provide valuable guidance for the design of MRSA drugs. This research might provide a new framework for designing new agents against the mutated form of PBP2a.Communicated by Ramaswamy H. Sarma.

Discrimination of Methicillin-resistant Staphylococcus aureus by MALDI-TOF Mass Spectrometry with Machine Learning Techniques in Patients with Staphylococcus aureus Bacteremia

Early administration of proper antibiotics is considered to improve the clinical outcomes of Staphylococcus aureus bacteremia (SAB), but routine clinical antimicrobial susceptibility testing takes an additional 24 h after species identification. Recent studies elucidated matrix-assisted laser desorption/ionization time-of-flight mass spectra to discriminate methicillin-resistant strains (MRSA) or even incorporated with machine learning (ML) techniques. However, no universally applicable mass peaks were revealed, which means that the discrimination model might need to be established or calibrated by local strains’ data. Here, a clinically feasible workflow was provided. We collected mass spectra from SAB patients over an 8-month duration and preprocessed by binning with reference peaks. Machine learning models were trained and tested by samples independently of the first six months and the following two months, respectively. The ML models were optimized by genetic algorithm (GA). The accuracy, sensitivity, specificity, and AUC of the independent testing of the best model, i.e., SVM, under the optimal parameters were 87%, 75%, 95%, and 87%, respectively. In summary, almost all resistant results were truly resistant, implying that physicians might escalate antibiotics for MRSA 24 h earlier. This report presents an attainable method for clinical laboratories to build an MRSA model and boost the performance using their local data.

FBPAII and rpoBC, the Two Novel Secreted Proteins Identified by the Proteomic Approach from a Comparative Study between Antibiotic-Sensitive and Antibiotic-Resistant Helicobacter pylori-Associated Gastritis Strains

Helicobacter pylori infection is the leading cause of chronic gastritis, which can develop into gastric cancer. Eliminating H. pylori infection with antibiotics achieves the prevention of gastric cancer. Currently, the prevalence of H. pylori resistance to clarithromycin and metronidazole, and the dual resistance to metronidazole and clarithromycin (C_R, M_R, and C/M_R, respectively), remains at a high level worldwide. As a means of exploring new candidate proteins for the management of H. pylori infection, secreted proteins from antibiotic-susceptible and antibiotic-resistant H. pylori-associated gastritis strains were obtained by in-solution tryptic digestion coupled with nano-liquid chromatography tandem mass spectrometry (nano-LC-MS/MS). A total of 583, 582, 590, and 578 differential expressed proteins were identified from C_R, M_R, C/M_R, and antibiotic-sensitive strain (S_S) samples, respectively. Of these, 23 overlapping proteins were found by Venn diagram analysis. Based on heat map analyses, the most and least differing protein expressions were observed from C/M_R strains and S_S strains, respectively. Of the proteins secreted by the S_S strain, only nine were found. After predicting the protein interaction with metronidazole and clarithromycin via the STITCH database, the two most interesting proteins were found to be rpoBC and FBPAII. After quantitative real-time reverse transcription PCR (qRT-PCR) analysis, a downregulation of rpoB from M_R strains was observed, suggesting a relationship of rpoB to metronidazole sensitivity. Inversely, an upregulation of fba from C_R, M_R, and C/M_R strains was noticed, suggesting the paradoxical expression of FBPAII and the fba gene. This report is the first to demonstrate the association of these two novel secreted proteins, namely, rpoBC and FBPAII, with antibiotic-sensitive H. pylori -associated gastritis strains.

Mutation-Based Antibiotic Resistance Mechanism in Methicillin-Resistant Staphylococcus aureus Clinical Isolates

β-Lactam antibiotics target penicillin-binding proteins and inhibit the synthesis of peptidoglycan, a crucial step in cell wall biosynthesis. Staphylococcus aureus acquires resistance against β-lactam antibiotics by producing a penicillin-binding protein 2a (PBP2a), encoded by the mecA gene. PBP2a participates in peptidoglycan biosynthesis and exhibits a poor affinity towards β-lactam antibiotics. The current study was performed to determine the diversity and the role of missense mutations of PBP2a in the antibiotic resistance mechanism. The methicillin-resistant Staphylococcus aureus (MRSA) isolates from clinical samples were identified using phenotypic and genotypic techniques. The highest frequency (60%, 18 out of 30) of MRSA was observed in wound specimens. Sequence variation analysis of the mecA gene showed four amino acid substitutions (i.e., E239K, E239R, G246E, and E447K). The E239R mutation was found to be novel. The protein-ligand docking results showed that the E239R mutation in the allosteric site of PBP2a induces conformational changes in the active site and, thus, hinders its interaction with cefoxitin. Therefore, the present report indicates that mutation in the allosteric site of PBP2a provides a more closed active site conformation than wide-type PBP2a and then causes the high-level resistance to cefoxitin.

Effects of a previously selected antibiotic resistance on mutations acquired during development of a second resistance in Escherichia coli

Background

The effect of mutations conferring antibiotic resistance can depend on the genetic background. To determine if a previously de novo acquired antibiotic resistance influences the adaptation to a second antibiotic, antibiotic resistance was selected for by exposure to stepwise increasing sublethal levels of amoxicillin, enrofloxacin, kanamycin, or tetracycline. E. coli populations adapted to either a single or two antibiotics sequentially were characterized using whole genome population sequencing and MIC measurements.

Results

In a wild-type background, adaptation to any of the antibiotics resulted in the appearance of well-known mutations, as well as a number of mutated genes not known to be associated with antibiotic resistance. Development of a second resistance in a strain with an earlier acquired resistance to a different antibiotic did not always result in the appearance of all mutations associated with resistance in a wild-type background. In general, a more varied set of mutations was acquired during secondary adaptation. The ability of E. coli to maintain the first resistance during this process depended on the combination of antibiotics used. The maintenance of mutations associated with resistance to the first antibiotic did not always predict the residual MIC for that compound.

Conclusions

In general, the data presented here indicate that adaptation to each antibiotic is unique and independent. The mutational trajectories available in already resistant cells appear more varied than in wild-type cells, indicating that the genetic background of E. coli influences resistance development. The observed mutations cannot always fully explain the resistance pattern observed, indicating a crucial role for adaptation on the gene expression level in de novo acquisition of antibiotic resistance.